Modern conversational AI systems often split functionality into multiple tools or sub-agents, each specialized for a task (e.g. search, booking, math, etc.). When a user sends a query, the system must interpret intent and dispatch it to the right tool/agent. There are two broad approaches: letting a general-purpose LLM handle intent detection itself, or using a dedicated router component. In practice, many practitioners use a hybrid: an initial “router” classifies the intent and then a specialized agent or tool handles the task. Below we survey best practices and examples of each approach, referencing frameworks like LangChain and Semantic Router.
A common approach is to have the LLM itself decide which tool or chain to invoke. For example, one can prompt the model to output a JSON field indicating the desired “tool” or “function” (using OpenAI’s function-calling or ChatGPT Pl
